{
"title": "Attribute Weighted Class Complexity: A New Metric for Measuring Cognitive Complexity of OO Systems",
"authors": "Dr. L. Arockiam, A. Aloysius",
"country": null,
"institution": null,
"volume": "58",
"journal": "International Journal of Computer, Electrical, Automation, Control and Information Engineering",
"pagesStart": 1151,
"pagesEnd": 1157,
"ISSN": "1307-6892",
"URL": "http:\/\/waset.org\/publications\/2886",
"abstract": "In general, class complexity is measured based on any\r\none of these factors such as Line of Codes (LOC), Functional points\r\n(FP), Number of Methods (NOM), Number of Attributes (NOA) and so on. There are several new techniques, methods and metrics with\r\nthe different factors that are to be developed by the researchers for calculating the complexity of the class in Object Oriented (OO)\r\nsoftware. Earlier, Arockiam et.al has proposed a new complexity measure namely Extended Weighted Class Complexity (EWCC)\r\nwhich is an extension of Weighted Class Complexity which is proposed by Mishra et.al. EWCC is the sum of cognitive weights of\r\nattributes and methods of the class and that of the classes derived. In EWCC, a cognitive weight of each attribute is considered to be 1.\r\nThe main problem in EWCC metric is that, every attribute holds the\r\nsame value but in general, cognitive load in understanding the\r\ndifferent types of attributes cannot be the same. So here, we are proposing a new metric namely Attribute Weighted Class Complexity\r\n(AWCC). In AWCC, the cognitive weights have to be assigned for the attributes which are derived from the effort needed to understand\r\ntheir data types. The proposed metric has been proved to be a better\r\nmeasure of complexity of class with attributes through the case studies and experiments",
"references": null,
"publisher": "World Academy of Science, Engineering and Technology",
"index": "International Science Index 58, 2011"
}